Dynamic query execution using estimated execution time

ABSTRACT

The subject technology receives a first query plan corresponding to a first query, the first query plan comprising a new query plan different than a previous query plan for the first query. The subject technology determines a value indicating an estimated improvement in execution time of the first query plan in comparison to a previous execution time of the previous query plan. The subject technology, in response to determining that the value is greater than a threshold value, attempting to execute the first query using the first query plan, the attempting comprising determining that a second query plan selected among a plurality of query plans has a second estimated execution time that is less than an estimated execution time of the first query plan, and executing the first query at a subsequent time using the second query plan.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.17/236,471, filed Apr. 21, 2021, entitled “Responsive Plan Stability”,which claims priority to U.S. Provisional Patent Application Ser. No.63/143,671, filed Jan. 29, 2021, entitled “Responsive Plan Stability”,and the contents of each are incorporated herein by reference in theirentireties for all purposes.

TECHNICAL FIELD

Embodiments of the disclosure relate generally to databases and, morespecifically, to enabling techniques for responsive query plan stabilityin an online data system(s).

BACKGROUND

Databases are widely used for data storage and access in computingapplications. A goal of database storage is to provide enormous sums ofinformation in an organized manner so that it can be accessed, managed,and updated. In a database, data may be organized into rows, columns,and tables. Databases are used by various entities and companies forstoring information that may need to be accessed or analyzed.

A cloud database is a network-based system used for data analysis andreporting that comprises a central repository of integrated data fromone or more disparate sources. A cloud database can store current andhistorical data that can be used for creating analytical reports for anenterprise based on data stored within databases of the enterprise. Tothis end, data warehouses typically provide business intelligence tools,tools to extract, transform, and load data into the repository, andtools to manage and retrieve metadata.

When certain information is to be extracted from a database, a querystatement may be executed against the database data. A cloud databasesystem processes the query and returns certain data according to one ormore query predicates that indicate what information should be returnedby the query. The data warehouse system extracts specific data from thedatabase and formats that data into a readable form.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will be understood more fully from the detaileddescription given below and from the accompanying drawings of variousembodiments of the disclosure.

FIG. 1 illustrates an example computing environment that includes anetwork-based data warehouse system in communication with a cloudstorage platform, in accordance with some embodiments of the presentdisclosure.

FIG. 2 is a block diagram illustrating components of a compute servicemanager, in accordance with some embodiments of the present disclosure.

FIG. 3 is a block diagram illustrating components of an executionplatform, in accordance with some embodiments of the present disclosure.

FIG. 4 is flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure.

FIG. 5 is flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure.

FIG. 6 is flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure.

FIG. 7 is flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure.

FIG. 8 is flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure.

FIG. 9 illustrates a diagrammatic representation of a machine in theform of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, in accordance with some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

Reference will now be made in detail to specific example embodiments forcarrying out the inventive subject matter. Examples of these specificembodiments are illustrated in the accompanying drawings, and specificdetails are set forth in the following description in order to provide athorough understanding of the subject matter. It will be understood thatthese examples are not intended to limit the scope of the claims to theillustrated embodiments. On the contrary, they are intended to coversuch alternatives, modifications, and equivalents as may be includedwithin the scope of the disclosure.

FIG. 1 illustrates an example computing environment 100 that includes adatabase system in the example form of a network-based data warehousesystem 102, in accordance with some embodiments of the presentdisclosure. To avoid obscuring the inventive subject matter withunnecessary detail, various functional components that are not germaneto conveying an understanding of the inventive subject matter have beenomitted from FIG. 1. However, a skilled artisan will readily recognizethat various additional functional components may be included as part ofthe computing environment 100 to facilitate additional functionalitythat is not specifically described herein. In other embodiments, thecomputing environment may comprise another type of network-baseddatabase system or a cloud data platform.

As shown, the computing environment 100 comprises the network-based datawarehouse system 102 in communication with a cloud storage platform 104(e.g., AWS®, Microsoft Azure Blob Storage®, or Google Cloud Storage),and a cloud credential store provider 106. The network-based datawarehouse system 102 is a network-based system used for reporting andanalysis of integrated data from one or more disparate sources includingone or more storage locations within the cloud storage platform 104. Thecloud storage platform 104 comprises a plurality of computing machinesand provides on-demand computer system resources such as data storageand computing power to the network-based data warehouse system 102.

The network-based data warehouse system 102 comprises a compute servicemanager 108, an execution platform 110, and one or more metadatadatabases 112. The network-based data warehouse system 102 hosts andprovides data reporting and analysis services to multiple clientaccounts.

The compute service manager 108 coordinates and manages operations ofthe network-based data warehouse system 102. The compute service manager108 also performs query optimization and compilation as well as managingclusters of computing services that provide compute resources (alsoreferred to as “virtual warehouses”). The compute service manager 108can support any number of client accounts such as end users providingdata storage and retrieval requests, system administrators managing thesystems and methods described herein, and other components/devices thatinteract with compute service manager 108.

The compute service manager 108 is also in communication with a clientdevice 114. The client device 114 corresponds to a user of one of themultiple client accounts supported by the network-based data warehousesystem 102. A user may utilize the client device 114 to submit datastorage, retrieval, and analysis requests to the compute service manager108.

The compute service manager 108 is also coupled to one or more metadatadatabases 112 that store metadata pertaining to various functions andaspects associated with the network-based data warehouse system 102 andits users. For example, a metadata database(s) 112 may include a summaryof data stored in remote data storage systems as well as data availablefrom a local cache. Additionally, a metadata database(s) 112 may includeinformation regarding how data is organized in remote data storagesystems (e.g., the cloud storage platform 104) and the local caches.Information stored by a metadata database(s) 112 allows systems andservices to determine whether a piece of data needs to be accessedwithout loading or accessing the actual data from a storage device.

As another example, a metadata database(s) 112 can store one or morecredential objects 115. In general, a credential object 115 indicatesone or more security credentials to be retrieved from a remotecredential store. For example, the credential store provider 106maintains multiple remote credential stores 118-1 to 118-N. Each of theremote credential stores 118-1 to 118-N may be associated with a useraccount and may be used to store security credentials associated withthe user account. A credential object 115 can indicate one of moresecurity credentials to be retrieved by the compute service manager 108from one of the remote credential stores 118-1 to 118-N(e.g., for use inaccessing data stored by the storage platform 104).

In an embodiment, a data structure can be utilized for storage ofdatabase metadata in the metadata database(s) 112. For example, such adata structure may be generated from metadata micro-partitions and maybe stored in a metadata cache memory. The data structure includes tablemetadata pertaining to database data stored across a table of thedatabase. The table may include multiple micro-partitions serving asimmutable storage devices that cannot be updated in-place. Each of themultiple micro-partitions can include numerous rows and columns makingup cells of database data. The table metadata may include a tableidentification and versioning information indicating, for example, howmany versions of the table have been generated over a time period, whichversion of the table includes the most up-to-date information, how thetable was changed over time, and so forth. A new table version may begenerated each time a transaction is executed on the table, where thetransaction may include a DML statement such as an insert, delete,merge, and/or update command. Each time a DML, statement is executed onthe table, and a new table version is generated, one or more newmicro-partitions may be generated that reflect the DML statement.

In an embodiment, the aforementioned table metadata includes globalinformation about the table of a specific version. The aforementioneddata structure further includes file metadata that includes metadataabout a micro-partition of the table. The terms “file” and“micro-partition” may each refer to a subset of database data and may beused interchangeably in some embodiments. The file metadata includesinformation about a micro-partition of the table. Further, metadata maybe stored for each column of each micro-partition of the table. Themetadata pertaining to a column of a micro-partition may be referred toas an expression property (EP) and may include any suitable informationabout the column, including for example, a minimum and maximum for thedata stored in the column, a type of data stored in the column, asubject of the data stored in the column, versioning information for thedata stored in the column, file statistics for all micro-partitions inthe table, global cumulative expressions for columns of the table, andso forth. Each column of each micro-partition of the table may includeone or more expression properties.

As mentioned above, a table of a database may include many rows andcolumns of data. One table may include millions of rows of data and maybe very large and difficult to store or read. A very large table may bedivided into multiple smaller files corresponding to micro-partitions.For example, one table may be divided into six distinctmicro-partitions, and each of the six micro-partitions may include aportion of the data in the table. Dividing the table data into multiplemicro-partitions helps to organize the data and to find where certaindata is located within the table.

In an embodiment, all data in tables is automatically divided into animmutable storage device referred to as a micro-partition. Themicro-partition may be considered a batch unit where eachmicro-partition has contiguous units of storage. By way of example, eachmicro-partition may contain between 50 MB and 500 MB of uncompresseddata (note that the actual size in storage may be smaller because datamay be stored compressed).

Groups of rows in tables may be mapped into individual micro-partitionsorganized in a columnar fashion. This size and structure allow forextremely granular selection of the micro-partitions to be scanned,which can be comprised of millions, or even hundreds of millions, ofmicro-partitions. This granular selection process may be referred toherein as “pruning” based on metadata as described further herein.

In an example, pruning involves using metadata to determine whichportions of a table, including which micro-partitions or micro-partitiongroupings in the table, are not pertinent to a query, and then avoidingthose non-pertinent micro-partitions when responding to the query andscanning only the pertinent micro-partitions to respond to the query.Metadata may be automatically gathered about all rows stored in amicro-partition, including: the range of values for each of the columnsin the micro-partition; the number of distinct values; and/or additionalproperties used for both optimization and efficient query processing. Inone embodiment, micro-partitioning may be automatically performed on alltables. For example, tables may be transparently partitioned using theordering that occurs when the data is inserted/loaded.

The micro-partitions as described herein can provide considerablebenefits for managing database data, finding database data, andorganizing database data. Each micro-partition organizes database datainto rows and columns and stores a portion of the data associated with atable. One table may have many micro-partitions. The partitioning of thedatabase data among the many micro-partitions may be done in any mannerthat makes sense for that type of data.

A query may be executed on a database table to find certain informationwithin the table. To respond to the query, a compute service manager 108scans the table to find the information requested by the query. Thetable may include millions and millions of rows, and it would be verytime consuming and it would require significant computing resources forthe compute service manager 108 to scan the entire table. Themicro-partition organization along with the systems, methods, anddevices for database metadata storage of the subject technology providesignificant benefits by at least shortening the query response time andreducing the amount of computing resources that are required forresponding to the query.

The compute service manager 108 may find the cells of database data byscanning database metadata. The multiple level database metadata of thesubject technology enable the compute service manager 108 to quickly andefficiently find the correct data to respond to the query. The computeservice manager 108 may find the correct table by scanning tablemetadata across all the multiple tables in a given database. The computeservice manager 108 may find a correct grouping of micro-partitions byscanning multiple grouping expression properties across the identifiedtable. Such grouping expression properties include information aboutdatabase data stored in each of the micro-partitions within thegrouping.

The compute service manager 108 may find a correct micro-partition byscanning multiple micro-partition expression properties within theidentified grouping of micro-partitions. The compute service manager 108may find a correct column by scanning one or more column expressionproperties within the identified micro-partition. The compute servicemanager 108 may find the correct row(s) by scanning the identifiedcolumn within the identified micro-partition. The compute servicemanager 108 may scan the grouping expression properties to findgroupings that have data based on the query. The compute service manager108 reads the micro-partition expression properties for that grouping tofind one or more individual micro-partitions based on the query. Thecompute service manager 108 reads column expression properties withineach of the identified individual micro-partitions. The compute servicemanager 108 scans the identified columns to find the applicable rowsbased on the query.

In an embodiment, an expression property is information about the one ormore columns stored within one or more micro-partitions. For example,multiple expression properties are stored that each pertain to a singlecolumn of a single micro-partition. In an alternative embodiment, one ormore expression properties are stored that pertain to multiple columnsand/or multiple micro-partitions and/or multiple tables. The expressionproperty is any suitable information about the database data and/or thedatabase itself. In an embodiment, the expression property includes oneor more of: a summary of database data stored in a column, a type ofdatabase data stored in a column, a minimum and maximum for databasedata stored in a column, a null count for database data stored in acolumn, a distinct count for database data stored in a column, astructural or architectural indication of how data is stored, and thelike.

In an embodiment, the metadata organization structures of the subjecttechnology may be applied to database “pruning” based on the metadata asdescribed further herein. The metadata organization may lead toextremely granular selection of pertinent micro-partitions of a table.Pruning based on metadata is executed to determine which portions of atable of a database include data that is relevant to a query. Pruning isused to determine which micro-partitions or groupings ofmicro-partitions are relevant to the query, and then scanning only thoserelevant micro-partitions and avoiding all other non-relevantmicro-partitions. By pruning the table based on the metadata, thesubject system can save significant time and resources by avoiding allnon-relevant micro-partitions when responding to the query. Afterpruning, the system scans the relevant micro-partitions based on thequery.

In an embodiment, the metadata database(s) 112 includes EP files(expression property files), where each of the EP files store acollection of expression properties about corresponding data. Metadatamay be stored for each column of each micro-partition of a given table.In an embodiment, the aforementioned EP files can be stored in a cacheprovided by the subject system for such EP files (e.g., “EP cache”).

The compute service manager 108 is further coupled to the executionplatform 110, which provides multiple computing resources that executevarious data storage and data retrieval tasks. The execution platform110 is coupled to storage platform 104 of the cloud storage platform104. The storage platform 104 comprises multiple data storage devices120-1 to 120-N. In some embodiments, the data storage devices 120-1 to120-N are cloud-based storage devices located in one or more geographiclocations. For example, the data storage devices 120-1 to 120-N may bepart of a public cloud infrastructure or a private cloud infrastructure.The data storage devices 120-1 to 120-N may be hard disk drives (HDDs),solid state drives (SSDs), storage clusters, Amazon S3TM storagesystems, or any other data storage technology. Additionally, the cloudstorage platform 104 may include distributed file systems (such asHadoop Distributed File Systems (HDFS)), object storage systems, and thelike.

The execution platform 110 comprises a plurality of compute nodes. A setof processes on a compute node executes a query plan compiled by thecompute service manager 108. The set of processes can include: a firstprocess to execute the query plan; a second process to monitor anddelete cache files using a least recently used (LRU) policy andimplement an out of memory (00M) error mitigation process; a thirdprocess that extracts health information from process logs and status tosend back to the compute service manager 108; a fourth process toestablish communication with the compute service manager 108 after asystem boot; and a fifth process to handle all communication with acompute cluster for a given job provided by the compute service manager108 and to communicate information back to the compute service manager108 and other compute nodes of the execution platform 110.

In some embodiments, communication links between elements of thecomputing environment 100 are implemented via one or more datacommunication networks. These data communication networks may utilizeany communication protocol and any type of communication medium. In someembodiments, the data communication networks are a combination of two ormore data communication networks (or sub-networks) coupled to oneanother. In alternate embodiments, these communication links areimplemented using any type of communication medium and any communicationprotocol.

The compute service manager 108, metadata database(s) 112, executionplatform 110, and storage platform 104, are shown in FIG. 1 asindividual discrete components. However, each of the compute servicemanager 108, metadata database(s) 112, execution platform 110, andstorage platform 104 may be implemented as a distributed system (e.g.,distributed across multiple systems/platforms at multiple geographiclocations). Additionally, each of the compute service manager 108,metadata database(s) 112, execution platform 110, and storage platform104 can be scaled up or down (independently of one another) depending onchanges to the requests received and the changing needs of thenetwork-based data warehouse system 102. Thus, in the describedembodiments, the network-based data warehouse system 102 is dynamic andsupports regular changes to meet the current data processing needs.

During typical operation, the network-based data warehouse system 102processes multiple jobs determined by the compute service manager 108.These jobs are scheduled and managed by the compute service manager 108to determine when and how to execute the job. For example, the computeservice manager 108 may divide the job into multiple discrete tasks andmay determine what data is needed to execute each of the multiplediscrete tasks. The compute service manager 108 may assign each of themultiple discrete tasks to one or more nodes of the execution platform110 to process the task. The compute service manager 108 may determinewhat data is needed to process a task and further determine which nodeswithin the execution platform 110 are best suited to process the task.Some nodes may have already cached the data needed to process the taskand, therefore, be a good candidate for processing the task. Metadatastored in a metadata database(s) 112 assists the compute service manager108 in determining which nodes in the execution platform 110 havealready cached at least a portion of the data needed to process thetask. One or more nodes in the execution platform 110 process the taskusing data cached by the nodes and, if necessary, data retrieved fromthe cloud storage platform 104. It is desirable to retrieve as much dataas possible from caches within the execution platform 110 because theretrieval speed is typically much faster than retrieving data from thecloud storage platform 104.

As shown in FIG. 1, the computing environment 100 separates theexecution platform 110 from the storage platform 104. In thisarrangement, the processing resources and cache resources in theexecution platform 110 operate independently of the data storage devices120-1 to 120-N in the cloud storage platform 104. Thus, the computingresources and cache resources are not restricted to specific datastorage devices 120-1 to 120-N. Instead, all computing resources and allcache resources may retrieve data from, and store data to, any of thedata storage resources in the cloud storage platform 104.

FIG. 2 is a block diagram illustrating components of the compute servicemanager 108, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 2, the compute service manager 108 includesan access manager 202 and a credential management system 204 coupled toan access metadata database 206, which is an example of the metadatadatabase(s) 112. Access manager 202 handles authentication andauthorization tasks for the systems described herein. The credentialmanagement system 204 facilitates use of remote stored credentials(e.g., credentials stored in one of the remote credential stores 118-1to 118-N) to access external resources such as data resources in aremote storage device. As used herein, the remote storage devices mayalso be referred to as “persistent storage devices” or “shared storagedevices.” For example, the credential management system 204 may createand maintain remote credential store definitions and credential objects(e.g., in the access metadata database 206). A remote credential storedefinition identifies a remote credential store (e.g., one or more ofthe remote credential stores 118-1 to 118-N) and includes accessinformation to access security credentials from the remote credentialstore. A credential object identifies one or more security credentialsusing non-sensitive information (e.g., text strings) that are to beretrieved from a remote credential store for use in accessing anexternal resource. When a request invoking an external resource isreceived at run time, the credential management system 204 and accessmanager 202 use information stored in the access metadata database 206(e.g., a credential object and a credential store definition) toretrieve security credentials used to access the external resource froma remote credential store.

A request processing service 208 manages received data storage requestsand data retrieval requests (e.g., jobs to be performed on databasedata). For example, the request processing service 208 may determine thedata to process a received query (e.g., a data storage request or dataretrieval request). The data may be stored in a cache within theexecution platform 110 or in a data storage device in storage platform104.

A management console service 210 supports access to various systems andprocesses by administrators and other system managers. Additionally, themanagement console service 210 may receive a request to execute a joband monitor the workload on the system.

The compute service manager 108 also includes a job compiler 212, a joboptimizer 214 and a job executor 216. The job compiler 212 parses a jobinto multiple discrete tasks and generates the execution code for eachof the multiple discrete tasks. The job optimizer 214 determines thebest method to execute the multiple discrete tasks based on the datathat needs to be processed. The job executor 216 executes the executioncode for jobs received from a queue or determined by the compute servicemanager 108.

A job scheduler and coordinator 218 sends received jobs to theappropriate services or systems for compilation, optimization, anddispatch to the execution platform 110. For example, jobs may beprioritized and then processed in that prioritized order. In anembodiment, the job scheduler and coordinator 218 determines a priorityfor internal jobs that are scheduled by the compute service manager 108with other “outside” jobs such as user queries that may be scheduled byother systems in the database but may utilize the same processingresources in the execution platform 110. In some embodiments, the jobscheduler and coordinator 218 identifies or assigns particular nodes inthe execution platform 110 to process particular tasks. A virtualwarehouse manager 220 manages the operation of multiple virtualwarehouses implemented in the execution platform 110. For example, thevirtual warehouse manager 220 may generate query plans for executingreceived queries. Alternatively or conjunctively, the job compiler 212can generate query plans for executing received queries as discussedfurther herein.

Additionally, the compute service manager 108 includes a configurationand metadata manager 222, which manages the information related to thedata stored in the remote data storage devices and in the local buffers(e.g., the buffers in execution platform 110). The configuration andmetadata manager 222 uses metadata to determine which data files need tobe accessed to retrieve data for processing a particular task or job. Amonitor and workload analyzer 224 oversee processes performed by thecompute service manager 108 and manages the distribution of tasks (e.g.,workload) across the virtual warehouses and execution nodes in theexecution platform 110. The monitor and workload analyzer 224 alsoredistributes tasks, as needed, based on changing workloads throughoutthe network-based data warehouse system 102 and may further redistributetasks based on a user (e.g., “external”) query workload that may also beprocessed by the execution platform 110. The configuration and metadatamanager 222 and the monitor and workload analyzer 224 are coupled to adata storage device 226. Data storage device 226 in FIG. 2 representsany data storage device within the network-based data warehouse system102. For example, data storage device 226 may represent buffers inexecution platform 110, storage devices in storage platform 104, or anyother storage device.

As described in embodiments herein, the compute service manager 108validates all communication from an execution platform (e.g., theexecution platform 110) to validate that the content and context of thatcommunication are consistent with the task(s) known to be assigned tothe execution platform. For example, an instance of the executionplatform executing a query A should not be allowed to request access todata-source D (e.g., data storage device 226) that is not relevant toquery A. Similarly, a given execution node (e.g., execution node 302-1may need to communicate with another execution node (e.g., executionnode 302-2), and should be disallowed from communicating with a thirdexecution node (e.g., execution node 312-1) and any such illicitcommunication can be recorded (e.g., in a log or other location). Also,the information stored on a given execution node is restricted to datarelevant to the current query and any other data is unusable, renderedso by destruction or encryption where the key is unavailable.

As further illustrated, the compute service manager 108 includes aresponsive plan engine 228. In an example, the responsive plan engine228 can implement (query) plan stability in a more immediate manner incomparison with existing solutions that may simply freeze plans (e.g.,disallowing any modification to the plans).

Embodiments of the responsive plan engine 228 to enable modifying plansto change immediately when there is a statistics change, but withfriction so that plans avoid swinging with a “small” statistics change,which involves the following:

-   -   In an example, the plan for a query changes with a statistics        change(s), but only if the cost with the new plan is less than        half the cost with the current one to impose friction to plan        changes.    -   When a new plan is tried (e.g., executed), the execution should        finish within the last execution time; otherwise, the execution        is canceled and then re-executed with the old (e.g., previous)        plan.    -   If the attempt to change the plan didn't go through, another        attempt is tried at a subsequent time if its cost is less than        half the cost of the last failed one.

In the description above, the cost and the execution time comparison canbe adjusted with a constant factor. The cost comparison can be adjustedto adjust the friction to plan change. The execution time comparison canbe adjusted to consider fluctuation in measurement. If the cost of thesame plan has been changed since the last execution, the last executiontime needs to be scaled as much. The execution time can be compared withthe maximum of the last few execution times instead of the lastexecution time. A plan change can be attempted a few times before it isdetermined to have failed.

One might think that the latency added with the retry can be avoided ifnew plans are validated in the background. However, updating plans afterbackground validation are unable to react to sudden statistics changes.The quickest response is from the next execution. It is also moreexpensive because such a validation may need an additional warming up toexclude the difference from caching effects. Moreover, the added latencyfrom retry is not expected to be salient. First, the probability ofretry is not expected to be high since a plan change is tried only witha significant cost change. Second, the added latency is limited to thelast execution time.

The following discussion relates to handling constant values, which maybe included in queries.

In some embodiments, for plan stability, the subject system stores aplan per query. More specifically, responsive plan engine 228 decideswhether to treat queries that are different only in constant values asthe same query or not. In an example, the constant values include notonly the ones embedded in the query string but also the ones set byparameter bindings.

The responsive plan engine 228 tries to maintain the required planstability while not missing the required plan difference at the sametime by treating constant value difference as statistics change asfollows:

-   -   If there is a stored plan matched in an unparameterized way, it        is taken as the base plan for the query. In this example, the        unparameterized way means the way of treating queries that are        different only in constant values as different ones.    -   Else if there is a stored plan matched in a parameterized way,        it is taken as the base plan for the query. In this example, the        parameterized way means the way of treating queries that are        different only in constant values as the same one.    -   The plan without plan stability is checked as if it is a new        plan with statistics change.

When there are multiple matches in a parameterized way, one way to breakthe tie could be choosing the one with the closest parameter values.However, the definition of “closest” gets ambiguous with multipleparameters. The subject system uses a simpler heuristic of choosing theone with the closest estimated cost.

The following discussion relates to storing plans for plan stability.

Although the subject system may need to store plans for plan stability,differently from plan caching, the subject system may not need to storethe full plan. In an example, the subject system may need to store onlyjoin orders because cost-based optimization decides only join orders.Additionally, the subject system may need to store more information ifthe subject system acquires additional optimizations based on costestimation. For example, if the subject system implements cost-basedCommon Table Expression (CTE) inlining in the future, the subject systemwill need to store the CTE inlininig decision together with join orderdecisions, making the CTE inlining decision replayable, too.

One benefit of not storing the full plan is that it requires much lessstorage. Another benefit is that the subject system is free to applydata-dependent optimization. It also allows minor plan changes forcorrectness fix and enables plan sharing between queries with differentconstant values. The subject system may need to compile the query toapply the stored join order, but the join ordering part will be muchcheaper with it because the expensive cost-based optimization is notneeded.

One issue with storing only join orders is that the subject system maynot be able to replay the same join order because the join graphs maynot be compatible with each other due to a subplan removal bydata-dependent optimizations. In an embodiment, the subject system isable to proceed just taking the new plan when such an issue happens, butit will also be beneficial to handle such cases as well. Oneimplementation to address the issue is to make it possible for joinorders to be enforced approximately even if there are some missingtables or additional tables. Another implementation postponesdata-dependent subplan removal to later phases so that it does notaffect join order recording and replaying for plan stability. Since theplan stability information may need to be persistent and shared globallywithin an account, it will need to be stored or indexed by a givendatabase (e.g., Foundation DB, and the like).

The following discussion relates to handling breaking changes, which maybe a result of changes to a given binary or code base associated withthe subject system.

When there is such a breaking change to a binary or code base for thesubject system e.g., that makes plan stability fail to work, thefollowing can be applied to avoid not breaking or worsening planstability:

-   -   If there are pending breaking changes that are not yet tried and        there is no need to change the plan due to statistics change,        the pending breaking changes are tried. The stored plan is tried        to be preserved, but it may not be possible.    -   When breaking changes are tried, the execution should finish        within 120% of the last execution time; otherwise, the execution        is canceled and then re-executed after rolling back the breaking        changes.

In the description above, the execution time comparison can be adjustedwith a constant factor to consider fluctuation in measurement. If thecost of the same plan has been changed since the last execution, thelast execution time needs to be scaled as much. Checking the cost changefor scaling should be performed without enabling the breaking changesince the breaking change may change the cost estimation. The executiontime can be compared with the maximum of the last few execution timesinstead of the last execution time. The pending breaking changes can betried a few times before it is determined to have failed.

To control the application of breaking changes, responsive plan engine228 first associates a version number to each query. In addition, eachbreaking change needs to be guarded by a parameter that is associatedwith a version number. It is to activate it on compilation of a queryonly if the version number associated with the query is greater than theone associated with the parameter (assuming that the version isincremented after submission of all breaking changes). Responsive planengine 228 also keeps the global last breaking change versioninformation to determine whether there is any new breaking change with anew version.

After releasing a breaking change, the developer (e.g., a user) cancheck whether there are detected performance regressions and fix theissues if there are any with the change. The fix needs to be released asanother breaking change so that it is tried together with the failedchange later. The developer can also disable the change in the code bychanging the default value and detaching the version number not to makeit mixed with later breaking changes. Disabling the change immediatelyby setting the parameter in system or account level will also bepossible. It will be useful when the change incurs issues like crash,but would not be useful for performance regression because it is alreadyhandled automatically. If there were false detections due to measurementfluctuation or infrastructure issues, the developer can enforce theapplication of the breaking changes.

The following discussion relates to performance regression techniques.

In some example, performance regression tracking can be a good additionto plan stability because the query matching for plan stability lays afoundation for it. If the binary versions between the previous and thecurrent execution are different and the current execution issignificantly slower than the previous one, responsive plan engine 228can report it through usage tracking and logging. It can be reportedtogether whether there was a plan change between the two runs so thatthe report can be filtered out depending on it. The binary version andthe execution time are already recorded because they are needed tohandle breaking changes. Being significantly slower can be defined astaking at least twice as much time.

If it is desirable not only to detect performance regression but also toroll back the change with it, responsive plan engine 228 can treat thechange as if it is a breaking change. However, it will be desirable notto abuse the mechanism since it may make it difficult to analyze thedetected performance regressions. It will be better to include only upto a few of such changes per release. Since the execution is cancelledin this case when a performance regression is detected, the executioncancellations instead of significant execution time increases will bereported through usage tracking and logging.

When a developer releases a feature that may affect performance, thedeveloper checks whether there are performance regressions detected.Responsive plan engine 228 can also implement a dashboard for it so thata sudden spike in performance regression detection can be easilynoticed. If there are, the developer should verify whether theregressions were caused by his or her changes. If there are regressionsconfirmed to be due to his or her changes, the developer can fix them.If the change was released as a breaking change to roll back the changewith performance regression, the fix also is released as a breakingchange as described above.

Responsive plan engine 228 can also think of filing JIRAs automaticallywith performance regression detection in an embodiment.

Candidate Query Selection

It is advantageous when responsive plan engine 228 can apply the planstability to all queries, but it could be too costly. In an example, areasonable compromise to mitigate computing costs (e.g., utilization ofcomputing resources) could be tracking queries with the followingproperties:

-   -   Contains joins with table size larger than 1 million rows and        has executed within a week    -   Contains joins with table size larger than 1 billion rows and        has executed within a month

The 10 second threshold is from the assumption that the overhead couldbe kept under 1% if it takes less than 100 ms to access stored plans.The 10M rows, 10 seconds and 7 days are suggested default values forconfigurable parameters in some examples.

For shorter queries, responsive plan engine 228 will need an in-memoryexemption set so that responsive plan engine 228 can skip them quickly.The exemption set does not need to be exact or shared between (virtual)warehouses. In addition, responsive plan engine 228 can manage only theparameterized versions of them.

Parameterized Matching

One way to support matching queries in a parameterized way ismaintaining both unparameterized and parameterized entries per query.Another way is organizing unparameterized entries in two levels so thatqueries that are different only in constant values are clustered to eachother. Embodiments of the subject technology uses the latter way sinceits space consumption could be lower.

Cost Estimation

To know when a plan change due to statistics change is needed,responsive plan engine 228 may generate a plan for it. This may incur anexpensive cost-based optimization, but it could be fine since responsiveplan engine 228 can save it from the actual plan generation ifresponsive plan engine 228 decides not to change the plan, which will bethe common case. However, if it turns out not to be good enough, anotherstrategy responsive plan engine 228 can take is comparing the costs withplans from greedy heuristics. Responsive plan engine 228 also need totry not to repeat important compilation steps before join ordering likescan set pruning by caching the results of the steps.

Execution Time Measurement

The issue of measuring the last execution time may get more complex thanit looks if there are multiple types of warehouses under an account andthe same query is executed in different types of warehouses. For suchcases, responsive plan engine 228 will need to record the execution timeper warehouse type. When checking the last execution time, if there is arecord from a matching warehouse type, it is used. If not, one from theclosest type can be used with scaling.

Temporary Table Handling

When there are structurally identical queries with different temporarytables, they will be treated as different queries. However, depending onthe customer's application, they could be desirable to be treated as thesame query. For example, a customer (PDX) drops and then recreates sometables using different names, and queries them using the same templateeveryday. One possible solution to the challenge could be anaccount-level parameter setting to specify a table name masking pattern.For example, responsive plan engine 228 can specify that the maskingpattern is 6 or more consecutive digits using a regular expression andignore that part on query matching.

The following is an example of plan information that can be stored in atleast one embodiment.

Search slice:Key information:

-   -   Account ID    -   Parameterized query text hash    -   Query parameter values hash        Information to plan stability with manual plan selection,    -   Plan hash    -   Last used plan cost: this is also used for parameterized query        matching    -   Creation date    -   Last used date    -   Expiration date: extended on access    -   Is parameterized matching enabled: a flag    -   Is manually added: a flag        Information for automatic plan stability    -   Plan change attempt count    -   Execution history: a list of {warehouse size, a list of {job ID,        cost, time}}    -   Friction factor: MAX_VALUE can be used to avoid automatic plan        change.        Information for handling breaking changes:    -   Last used binary version    -   Last rejected binary version    -   Binary change attempt count        Optional information:    -   Last job ID (e.g., for debugging purposes)

FIG. 3 is a block diagram illustrating components of the executionplatform 110, in accordance with some embodiments of the presentdisclosure. As shown in FIG. 3, the execution platform 110 includesmultiple virtual warehouses, including virtual warehouse 1, virtualwarehouse 2, and virtual warehouse n. Each virtual warehouse includesmultiple execution nodes that each include a data cache and a processor.The virtual warehouses can execute multiple tasks in parallel by usingthe multiple execution nodes. As discussed herein, the executionplatform 110 can add new virtual warehouses and drop existing virtualwarehouses in real-time based on the current processing needs of thesystems and users. This flexibility allows the execution platform 110 toquickly deploy large amounts of computing resources when needed withoutbeing forced to continue paying for those computing resources when theyare no longer needed. All virtual warehouses can access data from anydata storage device (e.g., any storage device in cloud storage platform104).

Although each virtual warehouse shown in FIG. 3 includes three executionnodes, a particular virtual warehouse may include any number ofexecution nodes. Further, the number of execution nodes in a virtualwarehouse is dynamic, such that new execution nodes are created whenadditional demand is present, and existing execution nodes are deletedwhen they are no longer necessary.

Each virtual warehouse is capable of accessing any of the data storagedevices 120-1 to 120-N shown in FIG. 1. Thus, the virtual warehouses arenot necessarily assigned to a specific data storage device 120-1 to120-N and, instead, can access data from any of the data storage devices120-1 to 120-N within the cloud storage platform 104. Similarly, each ofthe execution nodes shown in FIG. 3 can access data from any of the datastorage devices 120-1 to 120-N. In some embodiments, a particularvirtual warehouse or a particular execution node may be temporarilyassigned to a specific data storage device, but the virtual warehouse orexecution node may later access data from any other data storage device.

In the example of FIG. 3, virtual warehouse 1 includes three executionnodes 302-1, 302-2, and 302-n. Execution node 302-1 includes a cache304-1 and a processor 306-1. Execution node 302-2 includes a cache 304-2and a processor 306-2. Execution node 302-n includes a cache 304-n and aprocessor 306-n. Each execution node 302-1, 302-2, and 302-n isassociated with processing one or more data storage and/or dataretrieval tasks. For example, a virtual warehouse may handle datastorage and data retrieval tasks associated with an internal service,such as a clustering service, a materialized view refresh service, afile compaction service, a storage procedure service, or a file upgradeservice. In other implementations, a particular virtual warehouse mayhandle data storage and data retrieval tasks associated with aparticular data storage system or a particular category of data.

Similar to virtual warehouse 1 discussed above, virtual warehouse 2includes three execution nodes 312-1, 312-2, and 312-n. Execution node312-1 includes a cache 314-1 and a processor 316-1. Execution node 312-2includes a cache 314-2 and a processor 316-2. Execution node 312-nincludes a cache 314-n and a processor 316-n. Additionally, virtualwarehouse 3 includes three execution nodes 322-1, 322-2, and 322-n.Execution node 322-1 includes a cache 324-1 and a processor 326-1.Execution node 322-2 includes a cache 324-2 and a processor 326-2.Execution node 322-n includes a cache 324-n and a processor 326-n.

In some embodiments, the execution nodes shown in FIG. 3 are statelesswith respect to the data being cached by the execution nodes. Forexample, these execution nodes do not store or otherwise maintain stateinformation about the execution node or the data being cached by aparticular execution node. Thus, in the event of an execution nodefailure, the failed node can be transparently replaced by another node.Since there is no state information associated with the failed executionnode, the new (replacement) execution node can easily replace the failednode without concern for recreating a particular state.

Although the execution nodes shown in FIG. 3 each includes one datacache and one processor, alternate embodiments may include executionnodes containing any number of processors and any number of caches.Additionally, the caches may vary in size among the different executionnodes. The caches shown in FIG. 3 store, in the local execution node,data that was retrieved from one or more data storage devices in cloudstorage platform 104. Thus, the caches reduce or eliminate thebottleneck problems occurring in platforms that consistently retrievedata from remote storage systems. Instead of repeatedly accessing datafrom the remote storage devices, the systems and methods describedherein access data from the caches in the execution nodes, which issignificantly faster and avoids the bottleneck problem discussed above.In some embodiments, the caches are implemented using high-speed memorydevices that provide fast access to the cached data. Each cache canstore data from any of the storage devices in the cloud storage platform104.

Further, the cache resources and computing resources may vary betweendifferent execution nodes. For example, one execution node may containsignificant computing resources and minimal cache resources, making theexecution node useful for tasks that require significant computingresources. Another execution node may contain significant cacheresources and minimal computing resources, making this execution nodeuseful for tasks that require caching of large amounts of data. Yetanother execution node may contain cache resources providing fasterinput-output operations, useful for tasks that require fast scanning oflarge amounts of data. In some embodiments, the cache resources andcomputing resources associated with a particular execution node aredetermined when the execution node is created, based on the expectedtasks to be performed by the execution node.

Additionally, the cache resources and computing resources associatedwith a particular execution node may change over time based on changingtasks performed by the execution node. For example, an execution nodemay be assigned more processing resources if the tasks performed by theexecution node become more processor-intensive. Similarly, an executionnode may be assigned more cache resources if the tasks performed by theexecution node require a larger cache capacity.

Although virtual warehouses 1, 2, and n are associated with the sameexecution platform 110, the virtual warehouses may be implemented usingmultiple computing systems at multiple geographic locations. Forexample, virtual warehouse 1 can be implemented by a computing system ata first geographic location, while virtual warehouses 2 and n areimplemented by another computing system at a second geographic location.In some embodiments, these different computing systems are cloud-basedcomputing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown in FIG. 3 as havingmultiple execution nodes. The multiple execution nodes associated witheach virtual warehouse may be implemented using multiple computingsystems at multiple geographic locations. For example, an instance ofvirtual warehouse 1 implements execution nodes 302-1 and 302-2 on onecomputing platform at a geographic location and implements executionnode 302-n at a different computing platform at another geographiclocation. Selecting particular computing systems to implement anexecution node may depend on various factors, such as the level ofresources needed for a particular execution node (e.g., processingresource requirements and cache requirements), the resources availableat particular computing systems, communication capabilities of networkswithin a geographic location or between geographic locations, and whichcomputing systems are already implementing other execution nodes in thevirtual warehouse.

Execution platform 110 is also fault tolerant. For example, if onevirtual warehouse fails, that virtual warehouse is quickly replaced witha different virtual warehouse at a different geographic location.

A particular execution platform 110 may include any number of virtualwarehouses. Additionally, the number of virtual warehouses in aparticular execution platform is dynamic, such that new virtualwarehouses are created when additional processing and/or cachingresources are needed. Similarly, existing virtual warehouses may bedeleted (e.g., shut down) when the resources associated with the virtualwarehouse are no longer necessary.

In some embodiments, the virtual warehouses may operate on the same datain cloud storage platform 104, but each virtual warehouse has its ownexecution nodes with independent processing and caching resources. Thisconfiguration allows requests on different virtual warehouses to beprocessed independently and with no interference between the requests.This independent processing, combined with the ability to dynamicallyadd and remove virtual warehouses, supports the addition of newprocessing capacity for new users without significantly impacting theperformance observed by the existing users.

FIG. 4 is flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure. The method 400 may be embodied in computer-readableinstructions for execution by one or more hardware components (e.g., oneor more processors) such that the operations of the method 400 may beperformed by components of network-based data warehouse system 102, suchas components of the compute service manager 108. Accordingly, themethod 400 is described below, by way of example with reference thereto.However, it shall be appreciated that the method 400 may be deployed onvarious other hardware configurations and is not intended to be limitedto deployment within the network-based data warehouse system 102.

At operation 402, the responsive plan engine 228 receives a first queryplan corresponding to a first query, the first query plan comprising anew query plan different than a previous query plan for the first query.At operation 404, the responsive plan engine 228 determines a set ofconstants included in the first query. At operation 406, the responsiveplan engine 228 identifies a second query plan, corresponding to asecond query, the second query matching the first query and beingdifferent based on a second set of constants included in the secondquery. At operation 408, the responsive plan engine 228 executes thefirst query using the first query plan. At operation 410, the responsiveplan engine 228 that a first execution time of the first query isgreater than a second execution time of the second query. At operation412, the responsive plan engine 228 ceases execution of the first query.At operation 414, the responsive plan engine 228 executes the firstquery using the previous query plan.

FIG. 5 is flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure. The method 500 may be embodied in computer-readableinstructions for execution by one or more hardware components (e.g., oneor more processors) such that the operations of the method 500 may beperformed by components of network-based data warehouse system 102, suchas components of the compute service manager 108. Accordingly, themethod 500 is described below, by way of example with reference thereto.However, it shall be appreciated that the method 500 may be deployed onvarious other hardware configurations and is not intended to be limitedto deployment within the network-based data warehouse system 102.

At operation 502, the responsive plan engine 228 receives a first queryplan corresponding to a first query, the first query plan comprising anew query plan different than a previous query plan for the first query.At operation 504, the responsive plan engine 228 determines a set ofconstants included in the first query. At operation 506, the responsiveplan engine 228 determines a value indicating an estimated improvementin execution time of the first query plan in comparison to a previousexecution time of the previous query plan. At operation 508, theresponsive plan engine 228, in response to the value being greater thanthe threshold value, executes the first query using the first queryplan.

FIG. 6 is flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure. The method 600 may be embodied in computer-readableinstructions for execution by one or more hardware components (e.g., oneor more processors) such that the operations of the method 600 may beperformed by components of network-based data warehouse system 102, suchas components of the compute service manager 108. Accordingly, themethod 600 is described below, by way of example with reference thereto.However, it shall be appreciated that the method 600 may be deployed onvarious other hardware configurations and is not intended to be limitedto deployment within the network-based data warehouse system 102.

At operation 602, the responsive plan engine 228, during executing thefirst query, determines that a current execution time is greater thanthe previous execution time of the previous query plan. At operation604, the responsive plan engine 228, in response to determining that thecurrent execution time is greater than the previous execution time,ceases execution of the first query. At operation 606, the responsiveplan engine 228 performs a rollback operation to switch back to theprevious query plan for a subsequent execution of the first query. Atoperation 608, the responsive plan engine 228 increases the thresholdvalue in response to performing the rollback operation.

FIG. 7 is flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure. The method 700 may be embodied in computer-readableinstructions for execution by one or more hardware components (e.g., oneor more processors) such that the operations of the method 700 may beperformed by components of network-based data warehouse system 102, suchas components of the compute service manager 108. Accordingly, themethod 700 is described below, by way of example with reference thereto.However, it shall be appreciated that the method 700 may be deployed onvarious other hardware configurations and is not intended to be limitedto deployment within the network-based data warehouse system 102.

At operation 702, the responsive plan engine 228 determines a first setof constants in the first query plan. At operation 704, the responsiveplan engine 228 determines a second set of constants in the previousquery plan. At operation 706, the responsive plan engine 228 determineswhether differences between the first set of constants and the secondset of constants decrease an estimated execution time of the first queryplan in comparison with the previous execution time of the previousquery plan. At operation 708, the responsive plan engine 228 in responseto determining that the first set of constants increase the estimatedexecution time of the first query plan, perform a rollback operation toswitch back to the previous query plan for a subsequent execution of thefirst query.

FIG. 8 is flow diagram illustrating operations of a database system inperforming a method, in accordance with some embodiments of the presentdisclosure. The method 800 may be embodied in computer-readableinstructions for execution by one or more hardware components (e.g., oneor more processors) such that the operations of the method 800 may beperformed by components of network-based data warehouse system 102, suchas components of the compute service manager 108. Accordingly, themethod 800 is described below, by way of example with reference thereto.However, it shall be appreciated that the method 800 may be deployed onvarious other hardware configurations and is not intended to be limitedto deployment within the network-based data warehouse system 102.

At operation 802, the responsive plan engine 228 detects at least onenew change in software for query plan generation, the software utilizedat least in part for generating the first query plan. At operation 804,the responsive plan engine 228 activates the at least one new change. Atoperation 806, the responsive plan engine 228 deactivates the at leastone new change in response to in response to determining that thecurrent execution time is greater than the previous execution time.

FIG. 9 illustrates a diagrammatic representation of a machine 900 in theform of a computer system within which a set of instructions may beexecuted for causing the machine 900 to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 9 shows a diagrammatic representation of the machine900 in the example form of a computer system, within which instructions916 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 900 to perform any one ormore of the methodologies discussed herein may be executed. For example,the instructions 916 may cause the machine 900 to execute any one ormore operations of the method 400. In this way, the instructions 916transform a general, non-programmed machine into a particular machine900 (e.g., the compute service manager 108 or a node in the executionplatform 110) that is specially configured to carry out any one of thedescribed and illustrated functions in the manner described herein.

In alternative embodiments, the machine 900 operates as a standalonedevice or may be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 900 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 900 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a smart phone, a mobiledevice, a network router, a network switch, a network bridge, or anymachine capable of executing the instructions 916, sequentially orotherwise, that specify actions to be taken by the machine 900. Further,while only a single machine 900 is illustrated, the term “machine” shallalso be taken to include a collection of machines 900 that individuallyor jointly execute the instructions 916 to perform any one or more ofthe methodologies discussed herein.

The machine 900 includes processors 910, memory 930, and input/output(I/O) components 950 configured to communicate with each other such asvia a bus 902. In an example embodiment, the processors 910 (e.g., acentral processing unit (CPU), a reduced instruction set computing(RISC) processor, a complex instruction set computing (CISC) processor,a graphics processing unit (GPU), a digital signal processor (DSP), anapplication-specific integrated circuit (ASIC), a radio-frequencyintegrated circuit (RFIC), another processor, or any suitablecombination thereof) may include, for example, a processor 912 and aprocessor 914 that may execute the instructions 916. The term“processor” is intended to include multi-core processors 910 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions 916 contemporaneously. AlthoughFIG. 9 shows multiple processors 910, the machine 900 may include asingle processor with a single core, a single processor with multiplecores (e.g., a multi-core processor), multiple processors with a singlecore, multiple processors with multiple cores, or any combinationthereof.

The memory 930 may include a main memory 932, a static memory 934, and astorage unit 936, all accessible to the processors 910 such as via thebus 902. The main memory 932, the static memory 934, and the storageunit 936 store the instructions 916 embodying any one or more of themethodologies or functions described herein. The instructions 916 mayalso reside, completely or partially, within the main memory 932, withinthe static memory 934, within machine storage medium 938 of the storageunit 936, within at least one of the processors 910 (e.g., within theprocessor's cache memory), or any suitable combination thereof, duringexecution thereof by the machine 900.

The I/O components 950 include components to receive input, provideoutput, produce output, transmit information, exchange information,capture measurements, and so on. The specific I/O components 950 thatare included in a particular machine 900 will depend on the type ofmachine. For example, portable machines such as mobile phones willlikely include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 950 mayinclude many other components that are not shown in FIG. 9. The I/Ocomponents 950 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 950 mayinclude output components 952 and input components 954. The outputcomponents 952 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), other signal generators, and soforth. The input components 954 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 950 may include communication components 964 operableto couple the machine 900 to a network 980 or devices 970 via a coupling982 and a coupling 972, respectively. For example, the communicationcomponents 964 may include a network interface component or anothersuitable device to interface with the network 980. In further examples,the communication components 964 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, and other communication components to provide communicationvia other modalities. The devices 970 may be another machine or any of awide variety of peripheral devices (e.g., a peripheral device coupledvia a universal serial bus (USB)). For example, as noted above, themachine 900 may correspond to any one of the compute service manager 108or the execution platform 110, and the devices 970 may include theclient device 114 or any other computing device described herein asbeing in communication with the network-based data warehouse system 102or the cloud storage platform 104.

Executable Instructions and Machine Storage Medium

The various memories (e.g., 930, 932, 934, and/or memory of theprocessor(s) 910 and/or the storage unit 936) may store one or more setsof instructions 916 and data structures (e.g., software) embodying orutilized by any one or more of the methodologies or functions describedherein. These instructions 916, when executed by the processor(s) 910,cause various operations to implement the disclosed embodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably in this disclosure. The terms refer to a single ormultiple storage devices and/or media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storeexecutable instructions and/or data. The terms shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media, including memory internal or external toprocessors. Specific examples of machine-storage media, computer-storagemedia, and/or device-storage media include non-volatile memory,including by way of example semiconductor memory devices, e.g., erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), field-programmable gate arrays(FPGAs), and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The terms “machine-storage media,” “computer-storage media,” and“device-storage media” specifically exclude carrier waves, modulateddata signals, and other such media, at least some of which are coveredunder the term “signal medium” discussed below.

Transmission Medium

In various example embodiments, one or more portions of the network 980may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local-area network (LAN), a wireless LAN (WLAN), awide-area network (WAN), a wireless WAN (WWAN), a metropolitan-areanetwork (MAN), the Internet, a portion of the Internet, a portion of thepublic switched telephone network (PSTN), a plain old telephone service(POTS) network, a cellular telephone network, a wireless network, aWi-Fi® network, another type of network, or a combination of two or moresuch networks. For example, the network 980 or a portion of the network980 may include a wireless or cellular network, and the coupling 982 maybe a Code Division Multiple Access (CDMA) connection, a Global Systemfor Mobile communications (GSM) connection, or another type of cellularor wireless coupling. In this example, the coupling 982 may implementany of a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized(EVDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High-Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard-setting organizations, other long-rangeprotocols, or other data transfer technology.

The instructions 916 may be transmitted or received over the network 980using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components964) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions916 may be transmitted or received using a transmission medium via thecoupling 972 (e.g., a peer-to-peer coupling) to the devices 970. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 916 for execution by the machine 900, and include digitalor analog communications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of modulated datasignal, carrier wave, and so forth. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal.

Computer-Readable Medium

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Similarly, the methods described hereinmay be at least partially processor-implemented. For example, at leastsome of the operations of the method 400 may be performed by one or moreprocessors. The performance of certain of the operations may bedistributed among the one or more processors, not only residing within asingle machine, but also deployed across a number of machines. In someexample embodiments, the processor or processors may be located in asingle location (e.g., within a home environment, an office environment,or a server farm), while in other embodiments the processors may bedistributed across a number of locations.

CONCLUSION

Although the embodiments of the present disclosure have been describedwith reference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show, by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent, to those of skill inthe art, upon reviewing the above description.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim is still deemed to fall within thescope of that claim.

What is claimed is:
 1. A system comprising: at least one hardwareprocessor; and a memory storing instructions that cause the at least onehardware processor to perform operations comprising: receiving a firstquery plan corresponding to a first query, the first query plancomprising a new query plan different than a previous query plan for thefirst query; determining a value indicating an estimated improvement inexecution time of the first query plan in comparison to a previousexecution time of the previous query plan; and in response todetermining that the value is greater than a threshold value, attemptingto execute the first query using the first query plan, the attemptingcomprising: determining that a second query plan selected among aplurality of query plans has a second estimated execution time that isless than an estimated execution time of the first query plan; andexecuting the first query at a subsequent time using the second queryplan.
 2. The system of claim 1, wherein the operations further comprise:during execution of the first query using the first query plan,determining whether a current execution time of the first query plan isreduced in comparison with the previous execution time of the previousquery plan; and in response to determining that the current executiontime is not reduced, cancelling the execution and re-executing the firstquery using the previous query plan.
 3. The system of claim 2, whereinthe operations further comprise: in response to the determining that thecurrent execution time is not reduced, performing a rollback operationto switch back to the previous query plan for a subsequent execution ofthe first query, and the operations further comprise: increasing thethreshold value in response to the performing the rollback operation. 4.The system of claim 1, wherein the determining the value comprises:determining a first set of constants in the first query plan;determining a second set of constants in the previous query plan; anddetermining the value based at least in part on differences between thefirst set of constants and the second set of constants decreasing anestimated execution time of the first query plan in comparison with theprevious execution time of the previous query plan.
 5. The system ofclaim 4, wherein the first query includes at least one constant valueembedded in a first query string corresponding to the first query. 6.The system of claim 5, wherein the first set of constants includes theat least one constant value embedded in the first query string.
 7. Thesystem of claim 5, wherein the operations further comprise: in responseto determining that the first set of constants decrease the estimatedexecution time of the first query plan, selecting the first query planinstead of the previous query plan for execution.
 8. The system of claim7, wherein the operations further comprise: detecting at least one newchange in software for query plan selection, the software utilized atleast in part for the selecting the new query plan; and activating theat least one new change.
 9. The system of claim 8, wherein theoperations further comprise: deactivating the at least one new change inresponse to determining that a current execution time of the first queryplan is greater than the previous execution time of the previous queryplan.
 10. The system of claim 1, wherein the receiving the first queryplan comprises: tracking a plurality of queries corresponding to aplurality of query plans based on join operations contained in each ofthe plurality of queries and a previous time of executing each queryfrom the plurality of query plans, the previous query plan for the firstquery being included in the plurality of query plans, wherein, the joinoperations contained in each of the plurality of queries result in atable size larger than a particular number of rows and the previous timeof executing each query falls within a particular period of time,wherein the table size comprises a million number of rows and theparticular period of time comprises a week, and selecting the new queryplan among the plurality of query plans.
 11. A method comprising:receiving a first query plan corresponding to a first query, the firstquery plan comprising a new query plan different than a previous queryplan for the first query; determining a value indicating an estimatedimprovement in execution time of the first query plan in comparison to aprevious execution time of the previous query plan; and in response todetermining that the value is greater than a threshold value, attemptingto execute the first query using the first query plan, the attemptingcomprising: determining that a second query plan selected among aplurality of query plans has a second estimated execution time that isless than an estimated execution time of the first query plan; andexecuting the first query at a subsequent time using the second queryplan.
 12. The method of claim 11, further comprising: during executionof the first query using the first query plan, determining whether acurrent execution time of the first query plan is reduced in comparisonwith the previous execution time of the previous query plan; and inresponse to determining that the current execution time is not reduced,cancelling the execution and re-executing the first query using theprevious query plan.
 13. The method of claim 12, further comprising: inresponse to the determining that the current execution time is notreduced, performing a rollback operation to switch back to the previousquery plan for a subsequent execution of the first query, and furthercomprising: increasing the threshold value in response to the performingthe rollback operation.
 14. The method of claim 11, wherein thedetermining the value comprises: determining a first set of constants inthe first query plan; determining a second set of constants in theprevious query plan; and determining the value based at least in part ondifferences between the first set of constants and the second set ofconstants decreasing an estimated execution time of the first query planin comparison with the previous execution time of the previous queryplan.
 15. The method of claim 14, wherein the first query includes atleast one constant value embedded in a first query string correspondingto the first query.
 16. The method of claim 15, wherein the first set ofconstants includes the at least one constant value embedded in the firstquery string.
 17. The method of claim 15, further comprising: inresponse to determining that the first set of constants decrease theestimated execution time of the first query plan, selecting the firstquery plan instead of the previous query plan for execution.
 18. Themethod of claim 17, further comprising: detecting at least one newchange in software for query plan selection, the software utilized atleast in part for the selecting the new query plan; and activating theat least one new change.
 19. The method of claim 18, further comprising:deactivating the at least one new change in response to determining thata current execution time of the first query plan is greater than theprevious execution time of the previous query plan.
 20. The method ofclaim 11, wherein the receiving the first query plan comprises: trackinga plurality of queries corresponding to a plurality of query plans basedon join operations contained in each of the plurality of queries and aprevious time of executing each query from the plurality of query plans,the previous query plan for the first query being included in theplurality of query plans, wherein, the join operations contained in eachof the plurality of queries result in a table size larger than aparticular number of rows and the previous time of executing each queryfalls within a particular period of time, wherein the table sizecomprises a million number of rows and the particular period of timecomprises a week, and selecting the new query plan among the pluralityof query plans.
 21. A non-transitory computer-storage medium comprisinginstructions that, when executed by one or more processors of a machine,configure the machine to perform operations comprising: receiving afirst query plan corresponding to a first query, the first query plancomprising a new query plan different than a previous query plan for thefirst query; determining a value indicating an estimated improvement inexecution time of the first query plan in comparison to a previousexecution time of the previous query plan; and in response todetermining that the value is greater than a threshold value, attemptingto execute the first query using the first query plan, the attemptingcomprising: determining that a second query plan selected among aplurality of query plans has a second estimated execution time that isless than an estimated execution time of the first query plan; andexecuting the first query at a subsequent time using the second queryplan.
 22. The non-transitory computer-storage medium claim 21, whereinthe operations further comprise: during execution of the first queryusing the first query plan, determining whether a current execution timeof the first query plan is reduced in comparison with the previousexecution time of the previous query plan; and in response todetermining that the current execution time is not reduced, cancellingthe execution and re-executing the first query using the previous queryplan.
 23. The non-transitory computer-storage medium claim 22, whereinthe operations further comprise: in response to the determining that thecurrent execution time is not reduced, performing a rollback operationto switch back to the previous query plan for a subsequent execution ofthe first query, and the operations further comprise: increasing thethreshold value in response to the performing the rollback operation.24. The non-transitory computer-storage medium claim 21, wherein thedetermining the value comprises: determining a first set of constants inthe first query plan; determining a second set of constants in theprevious query plan; and determining the value based at least in part ondifferences between the first set of constants and the second set ofconstants decreasing an estimated execution time of the first query planin comparison with the previous execution time of the previous queryplan.
 25. The non-transitory computer-storage medium claim 24, whereinthe first query includes at least one constant value embedded in a firstquery string corresponding to the first query.
 26. The non-transitorycomputer-storage medium claim 25, wherein the first set of constantsincludes the at least one constant value embedded in the first querystring.
 27. The non-transitory computer-storage medium claim 25, whereinthe operations further comprise: in response to determining that thefirst set of constants decrease the estimated execution time of thefirst query plan, selecting the first query plan instead of the previousquery plan for execution.
 28. The non-transitory computer-storage mediumclaim 27, wherein the operations further comprise: detecting at leastone new change in software for query plan selection, the softwareutilized at least in part for the selecting the new query plan; andactivating the at least one new change.
 29. The non-transitorycomputer-storage medium claim 28, wherein the operations furthercomprise: deactivating the at least one new change in response todetermining that a current execution time of the first query plan isgreater than the previous execution time of the previous query plan. 30.The non-transitory computer-storage medium claim 21, wherein thereceiving the first query plan comprises: tracking a plurality ofqueries corresponding to a plurality of query plans based on joinoperations contained in each of the plurality of queries and a previoustime of executing each query from the plurality of query plans, theprevious query plan for the first query being included in the pluralityof query plans, wherein, the join operations contained in each of theplurality of queries result in a table size larger than a particularnumber of rows and the previous time of executing each query fallswithin a particular period of time, wherein the table size comprises amillion number of rows and the particular period of time comprises aweek, and selecting the new query plan among the plurality of queryplans.